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Limited interventions exist for PICS, and evidence suggests that exposure to visual art can help, especially when personalized. The work develops machine learning-based visual art recommendation systems (VARecSys) for therapeutic art experiences after ICU discharge. Four state-of-the-art recommendation engines are evaluated against expert-curated recommendations via a pilot and a large-scale study (n=150). 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It focuses on limitations of current PICS interventions.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How are machine learning recommendation systems used in the study?",{"text":117,"@type":113},"The study proposes VARecSys to deliver personalized visual art experiences for post-ICU patients. It evaluates recommendations generated by four state-of-the-art recommender engines.",{"name":119,"@type":110,"acceptedAnswer":120},"What outcomes are measured and what do the results indicate?",{"text":121,"@type":113},"The study measures art-therapy relevant engagement and immersion, along with PICS-related temporal enhancement of affective state. 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Yilma  \n[bereket.yilma@uni.lu](bereket.yilma@uni.lu)[ ](bereket.yilma@uni.lu)University of Luxembourg Luxembourg  \nChan Mi Kim  \n[c.m.kim@utwente.nl](c.m.kim@utwente.nl)[ ](c.m.kim@utwente.nl)University of Twente The Netherlands  \nPreference Elicitation Recommended Paintings  \nBaseline measures  \nParticipants chooses one of the sample paintings  \nParticipants are randomly assigned to one of the recommendation sets  \nGerald C. Cupchik Luis A. Leiva  \n[gerald.cupchik@utoronto.ca](gerald.cupchik@utoronto.ca) luis.leiva@uni.lu  \nUniversity of Toronto University of Luxembourg  \nCanada Luxembourg  \nGuided therapy using the recommended paintings  \nOutcome measures  \nArt therapy relevant measures:  \n Openness to engagement with paintings  \n Level of immersion in paintings  \nPICS relevant measures:  \n (Temporal) enhancement of affective state  \nVA RecSys relevant measures:  \n Accuracy, Diversity, Novelity, Serendipity  \nFigure 1: Overview of our art therapy approach for PICS prevention and treatment.  \nABSTRACT  \nStaying in the intensive care unit (ICU) is often traumatic, leading to post-intensive care syndrome (PICS), which encompasses physical, psychological, and cognitive impairments. Currently, there are limited interventions available for PICS. Studies indicate that exposure to visual art may help address the psychological aspects of PICS and be more effective if it is personalized. We develop Machine Learning-based Visual Art Recommendation Systems (VARecSys) to enable personalized therapeutic visual art experiences for post-ICU patients. We investigate four state-of-the-art VA RecSys engines, evaluating the relevance of their recommendations for therapeutic purposes compared to expert-curated recommendations. We conduct an expert pilot test and a large-scale user study (n=150) to assess the appropriateness and effectiveness of these recommendations. Our results suggest all recommendations enhance temporal affective states. Visual and multimodal VA RecSys engines compare favourably with expert-curated recommendations, indicating their potential to support the delivery of personalized art therapy for PICS prevention and treatment.  \nThis work is licensed under a Creative Commons Attribution International 4.0 License.  \nCHI’24, May 11–16, 2024, Honolulu, HI, USA © 2024 Copyright held by the owner/author(s) .  \nACM ISBN 979-8-4007-0330-0/24/05  \n[https://doi.org/10.1145/3613904.3642636](https://doi.org/10.1145/3613904.3642636)  \nCCS CONCEPTS  \n• Information systems → Personalization; Recommender systems; • Computing methodologies → Learning latent representations; • Applied computing → Media arts.  \nKEYWORDS  \nRecommendation; Personalization; Artwork; User Experience; Machine Learning; intensive care unit; rehabilitation; Health  \nACM Reference Format:  \nBereket A. Yilma, Chan Mi Kim, Gerald C. Cupchik, and Luis A. Leiva. 2024. Artful Path to Healing: Using Machine Learning for Visual Art Recommendation to Prevent and Reduce Post-Intensive Care Syndrome (PICS) . In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI’24), May 11–16, 2024, Honolulu, HI, USA. ACM, New York, NY, USA, 19 pages. [https://doi.org/10.1145/3613904.3642636](https://doi.org/10.1145/3613904.3642636)  \n1 INTRODUCTION  \nPatients in the intensive care unit (ICU) generally undergo stressful and traumatic experiences stemming from critical illness, medical procedures, pain, and a hostile environment [20] . Even after ICU discharge, these patients are vulnerable and at a high risk of readmission to the hospital and the ICU [70, 80]. Post-ICU patients often suffer from post-intensive care syndrome (PICS) which refers to“new or worsening impairments in physical, cognitive, or mental health arising after critical illness and persisting beyond acute care hospitalization” [55] . PICS is quite common","cbCaidAk4FpBs5YH","https://ap.wps.com/l/cbCaidAk4FpBs5YH","pdf",7306168,"English","# Abstract\n# Introduction\n## Post-intensive care syndrome (PICS) and current interventions\n## Visual art and art therapy for psychological well-being\n## Personalization and recommendation systems for therapeutic effectiveness","[{\"question\":\"What problem does this work address?\",\"answer\":\"The work targets post-intensive care syndrome (PICS), which includes impairments in physical, psychological, or cognitive health after critical illness. It focuses on limitations of current PICS interventions.\"},{\"question\":\"How are machine learning recommendation systems used in the study?\",\"answer\":\"The study proposes VARecSys to deliver personalized visual art experiences for post-ICU patients. It evaluates recommendations generated by four state-of-the-art recommender engines.\"},{\"question\":\"What outcomes are measured and what do the results indicate?\",\"answer\":\"The study measures art-therapy relevant engagement and immersion, along with PICS-related temporal enhancement of affective state. It finds that all recommendation sets improve temporal affective states and that VA RecSys engines compare favorably with expert-curated recommendations.\"}]","Artful Path to Healing - Using Machine Learning for Visual Art Recommendation to Prevent and Reduce Post-Intensive Care Syndrome | PDF",48]